1. Field of the Invention
The present invention relates to a method of data aggregation and correlation in a sensor network, and a sensor node that implements the method of the data aggregation and correlation.
2. Description of the Related Art
Sensor network nodes have restrictions on resources such as energy, memory, processor speed due to size and cost constraints. Current sensor goals target a sensor size less than 1 mm3. Future sensors will be dust-size. These constraints limit the amount of memory for program and data storage as well as the number of information symbols that can be processed and transmitted.
A principal cause of energy use in a sensor network is data transmission from multiple sensor nodes, many of which may report the same information. Hence, sensor nodes implement data-centric forwarding techniques to reduce unnecessary data transmission. Reasons for data transmission removal include such factors as duplication, out-of-range, or errors in data. Further reductions occur with averaging and correlation techniques. For example, a series of sensing processes that read the same values can be concisely described by an average with a zero standard deviation. The role of data-centric forwarding technique embodies the application of a data aggregation algorithm that operates on the data in-route to the data sink from different sensor nodes in order to remove unnecessary data. Various types of functions are executed in the algorithm.
The focus of topology formation and routing shifts from the traditional address-centric approaches for networking, which is to find short routes between pairs of addressable end-nodes, to a more data-centric approach, which is to find routes from multiple sources to a single destination that allows in-network consolidation of redundant data. Hence, efficient data-centric forwarding technique requires that data aggregation operates to establish the appropriate combination of aggregation operators to optimize energy conservation.
A commonly used tool for data-centric forwarding technique and data aggregation is TinyDB. TinyDB is a query processing system for extracting information from a network of sensors that employs TinyOS as the operating system. It provides a simple, SQL-like interface to specify the data that needs extraction, along with additional parameters, like the rate at which data should be refreshed. The SQL interface supports queries for min, max, sum, count, and average. Given a query that specifies the data of interest, TinyDB collects the data from sensors in the environment, filters it, aggregates it together, and routes it out to a sink that hosts the TinyDB server software.
TinyDB, however, has several disadvantages, which includes that TinyDB uses TinyOS operating system and requires up to 58 KB of program memory, TinyDB employs query types limited to current types of deployed sensors, SQL interface does not support (MIN, MAX) filtering during data aggregation, TinyDB lacks provisions for temporal and spatial correlation and lacks programmability for efficient temporal convolution or filtering.
Therefore, it is necessary to provide a method for a data aggregation algorithm with a structure that supports a greater range of functions with lower program memory requirements and lower processing requirements.